1
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Abe K. Dynamic activity changes in transcription factors: Unlocking the mechanisms regulating physiological changes in the brain. Neurosci Res 2024:S0168-0102(24)00101-9. [PMID: 39134224 DOI: 10.1016/j.neures.2024.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2024] [Revised: 07/30/2024] [Accepted: 08/04/2024] [Indexed: 08/18/2024]
Abstract
Transcription factors (TFs) regulate the establishment and modulation of the transcriptome within cells, thereby playing a crucial role in various aspects of cellular physiology throughout the body. Quantitative measurement of TF activity during the development, function, and dysfunction of the brain is essential for gaining a deeper understanding of the regulatory mechanisms governing gene expression during these processes. Due to their role as regulators of gene expression, assessing and modulating detailed TF activity contributes to the development of practical methods to intervene in these processes, potentially offering more efficient treatments for diseases. Recent methodologies have revealed that TF activity is dynamically regulated within cells and organisms, including the adult brain. This review summarizes the regulatory mechanisms of TF activities and the methodologies used to assess them, emphasizing their importance in both fundamental research and clinical applications.
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Affiliation(s)
- Kentaro Abe
- Lab of Brain Development, Graduate School of Life Sciences, Tohoku University, Katahira 2-1-1, Aoba-ku, Sendai, Miyagi 980-8577, Japan; Division for the Establishment of Frontier Sciences of the Organization for Advanced Studies, Tohoku University, Katahira 2-1-1, Aoba-ku, Sendai, Miyagi 980-8577, Japan.
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2
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Chen C, Padi M. Flexible modeling of regulatory networks improves transcription factor activity estimation. NPJ Syst Biol Appl 2024; 10:58. [PMID: 38806476 PMCID: PMC11133322 DOI: 10.1038/s41540-024-00386-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 05/13/2024] [Indexed: 05/30/2024] Open
Abstract
Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data remains a significant challenge. Existing methods for estimating transcription factor (TF) activity often rely on static TF-gene interaction databases and cannot adapt to changes in regulatory mechanisms across different cell types and disease conditions. Here, we present a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that overcomes these limitations by flexibly modeling activation and inhibition events, up-weighting essential edges, shrinking irrelevant edges towards zero through a sparse Bayesian prior, and simultaneously estimating both TF activity levels and changes in the underlying regulatory network. When applied to yeast and cancer TF knock-out datasets, TIGER outperforms comparable methods in terms of prediction accuracy. Moreover, our application of TIGER to tissue- and cell-type-specific RNA-seq data demonstrates its ability to uncover differences in regulatory mechanisms. Collectively, our findings highlight the utility of modeling context-specific regulation when inferring transcription factor activities.
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Affiliation(s)
- Chen Chen
- Department of Epidemiology and Biostatistics, University of Arizona Mel and Enid Zuckerman College of Public Health, Tucson, AZ, USA
- University of Arizona Cancer Center, University of Arizona, Tucson, AZ, USA
| | - Megha Padi
- University of Arizona Cancer Center, University of Arizona, Tucson, AZ, USA.
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA.
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3
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Daldoul S, Hanzouli F, Boubakri H, Nick P, Mliki A, Gargouri M. Deciphering the regulatory networks involved in mild and severe salt stress responses in the roots of wild grapevine Vitis vinifera spp. sylvestris. PROTOPLASMA 2024; 261:447-462. [PMID: 37963978 DOI: 10.1007/s00709-023-01908-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 11/06/2023] [Indexed: 11/16/2023]
Abstract
Transcriptional regulatory networks are pivotal components of plant's response to salt stress. However, plant adaptation strategies varied as a function of stress intensity, which is mainly modulated by climate change. Here, we determined the gene regulatory networks based on transcription factor (TF) TF_gene co-expression, using two transcriptomic data sets generated from the salt-tolerant "Tebaba" roots either treated with 50 mM NaCl (mild stress) or 150 mM NaCl (severe stress). The analysis of these regulatory networks identified specific TFs as key regulatory hubs as evidenced by their multiple interactions with different target genes related to stress response. Indeed, under mild stress, NAC and bHLH TFs were identified as central hubs regulating nitrogen storage process. Moreover, HSF TFs were revealed as a regulatory hub regulating various aspects of cellular metabolism including flavonoid biosynthesis, protein processing, phenylpropanoid metabolism, galactose metabolism, and heat shock proteins. These processes are essentially linked to short-term acclimatization under mild salt stress. This was further consolidated by the protein-protein interaction (PPI) network analysis showing structural and plant growth adjustment. Conversely, under severe salt stress, dramatic metabolic changes were observed leading to novel TF members including MYB family as regulatory hubs controlling isoflavonoid biosynthesis, oxidative stress response, abscisic acid signaling pathway, and proteolysis. The PPI network analysis also revealed deeper stress defense changes aiming to restore plant metabolic homeostasis when facing severe salt stress. Overall, both the gene co-expression and PPI network provided valuable insights on key transcription factor hubs that can be employed as candidates for future genetic crop engineering programs.
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Affiliation(s)
- Samia Daldoul
- Laboratory of Plant Molecular Physiology, Centre of Biotechnology of Borj-Cedria, BP. 901, Hammam-Lif, Tunisia.
| | - Faouzia Hanzouli
- Laboratory of Plant Molecular Physiology, Centre of Biotechnology of Borj-Cedria, BP. 901, Hammam-Lif, Tunisia
- Faculty of Sciences of Tunis, University Tunis El-Manar, El Manar II, 2092, Tunis, Tunisia
| | - Hatem Boubakri
- Laboratory of Legumes and Sustainable Agrosystems, Centre of Biotechnology of Borj-Cedria, B.P 901, 2050, Hammam-Lif, Tunisia
| | - Peter Nick
- Molecular Cell Biology, Botanical Institute, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Ahmed Mliki
- Laboratory of Plant Molecular Physiology, Centre of Biotechnology of Borj-Cedria, BP. 901, Hammam-Lif, Tunisia
| | - Mahmoud Gargouri
- Laboratory of Plant Molecular Physiology, Centre of Biotechnology of Borj-Cedria, BP. 901, Hammam-Lif, Tunisia.
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4
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Habibpour M, Razaghi-Moghadam Z, Nikoloski Z. Prediction and integration of metabolite-protein interactions with genome-scale metabolic models. Metab Eng 2024; 82:216-224. [PMID: 38367764 DOI: 10.1016/j.ymben.2024.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/13/2024] [Accepted: 02/14/2024] [Indexed: 02/19/2024]
Abstract
Metabolites, as small molecules, can act not only as substrates to enzymes, but also as effectors of activity of proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started to catalogue the metabolite-protein interactions (MPIs) present in different cellular contexts, characterizing the functional relevance of MPIs remains a challenging problem. Computational approaches from the constrained-based modeling framework allow for predicting MPIs and integrating their effects in the in silico analysis of metabolic and physiological phenotypes, like cell growth. Here, we provide a classification of all existing constraint-based approaches that predict and integrate MPIs using genome-scale metabolic networks as input. In addition, we benchmark the performance of the approaches to predict MPIs in a comparative study using different features extracted from the model structure and predicted metabolic phenotypes with the state-of-the-art metabolic networks of Escherichia coli and Saccharomyces cerevisiae. Lastly, we provide an outlook for future, feasible directions to expand the consideration of MPIs in constraint-based modeling approaches with wide biotechnological applications.
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Affiliation(s)
- Mahdis Habibpour
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | - Zahra Razaghi-Moghadam
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany; Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany; Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany.
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5
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Hecker D, Lauber M, Behjati Ardakani F, Ashrafiyan S, Manz Q, Kersting J, Hoffmann M, Schulz MH, List M. Computational tools for inferring transcription factor activity. Proteomics 2023; 23:e2200462. [PMID: 37706624 DOI: 10.1002/pmic.202200462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 08/11/2023] [Accepted: 08/22/2023] [Indexed: 09/15/2023]
Abstract
Transcription factors (TFs) are essential players in orchestrating the regulatory landscape in cells. Still, their exact modes of action and dependencies on other regulatory aspects remain elusive. Since TFs act cell type-specific and each TF has its own characteristics, untangling their regulatory interactions from an experimental point of view is laborious and convoluted. Thus, there is an ongoing development of computational tools that estimate transcription factor activity (TFA) from a variety of data modalities, either based on a mapping of TFs to their putative target genes or in a genome-wide, gene-unspecific fashion. These tools can help to gain insights into TF regulation and to prioritize candidates for experimental validation. We want to give an overview of available computational tools that estimate TFA, illustrate examples of their application, debate common result validation strategies, and discuss assumptions and concomitant limitations.
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Affiliation(s)
- Dennis Hecker
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Michael Lauber
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Fatemeh Behjati Ardakani
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Shamim Ashrafiyan
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Quirin Manz
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Johannes Kersting
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
- GeneSurge GmbH, München, Germany
| | - Markus Hoffmann
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Advanced Study, Technical University of Munich, Garching, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Marcel H Schulz
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Markus List
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
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Su L, Zhang S, McGaughey AJH, Reeja‐Jayan B, Manthiram A. Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2301737. [PMID: 37394730 PMCID: PMC10502833 DOI: 10.1002/advs.202301737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/31/2023] [Indexed: 07/04/2023]
Abstract
Real-time onboard state monitoring and estimation of a battery over its lifetime is indispensable for the safe and durable operation of battery-powered devices. In this study, a methodology to predict the entire constant-current cycling curve with limited input information that can be collected in a short period of time is developed. A total of 10 066 charge curves of LiNiO2 -based batteries at a constant C-rate are collected. With the combination of a feature extraction step and a multiple linear regression step, the method can accurately predict an entire battery charge curve with an error of < 2% using only 10% of the charge curve as the input information. The method is further validated across other battery chemistries (LiCoO2 -based) using open-access datasets. The prediction error of the charge curves for the LiCoO2 -based battery is around 2% with only 5% of the charge curve as the input information, indicating the generalization of the developed methodology for predicting battery cycling curves. The developed method paves the way for fast onboard health status monitoring and estimation for batteries during practical applications.
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Affiliation(s)
- Laisuo Su
- Materials Science and Engineering Program & Texas Materials InstituteThe University of Texas at AustinAustinTX78712‐1591USA
| | - Shuyan Zhang
- Department of Mechanical EngineeringCarnegie Mellon UniversityPittsburghPA15213USA
| | - Alan J. H. McGaughey
- Department of Mechanical EngineeringCarnegie Mellon UniversityPittsburghPA15213USA
| | - B. Reeja‐Jayan
- Department of Mechanical EngineeringCarnegie Mellon UniversityPittsburghPA15213USA
| | - Arumugam Manthiram
- Materials Science and Engineering Program & Texas Materials InstituteThe University of Texas at AustinAustinTX78712‐1591USA
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7
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Qiao Q, Yuan SS, Shang J, Liu JX. Multi-View Enhanced Tensor Nuclear Norm and Local Constraint Model for Cancer Clustering and Feature Gene Selection. J Comput Biol 2023; 30:889-899. [PMID: 37471239 DOI: 10.1089/cmb.2023.0107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023] Open
Abstract
The analysis of cancer data from multi-omics can effectively promote cancer research. The main focus of this article is to cluster cancer samples and identify feature genes to reveal the correlation between cancers and genes, with the primary approach being the analysis of multi-view cancer omics data. Our proposed solution, the Multi-View Enhanced Tensor Nuclear Norm and Local Constraint (MVET-LC) model, aims to utilize the consistency and complementarity of omics data to support biological research. The model is designed to maximize the utilization of multi-view data and incorporates a nuclear norm and local constraint to achieve this goal. The first step involves introducing the concept of enhanced partial sum of tensor nuclear norm, which significantly enhances the flexibility of the tensor nuclear norm. After that, we incorporate total variation regularization into the MVET-LC model to further augment its performance. It enables MVET-LC to make use of the relationship between tensor data structures and sparse data while paying attention to the feature details of the tensor data. To tackle the iterative optimization problem of MVET-LC, the alternating direction method of multipliers is utilized. Through experimental validation, it is demonstrated that our proposed model outperforms other comparison models.
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Affiliation(s)
- Qian Qiao
- School of Computer Science, Qufu Normal University, Rizhao, China
| | - Sha-Sha Yuan
- School of Computer Science, Qufu Normal University, Rizhao, China
| | - Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao, China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University, Rizhao, China
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8
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Chen X, Wang Y, Cappuccio A, Cheng WS, Zamojski FR, Nair VD, Miller CM, Rubenstein AB, Nudelman G, Tadych A, Theesfeld CL, Vornholt A, George MC, Ruffin F, Dagher M, Chawla DG, Soares-Schanoski A, Spurbeck RR, Ndhlovu LC, Sebra R, Kleinstein SH, Letizia AG, Ramos I, Fowler VG, Woods CW, Zaslavsky E, Troyanskaya OG, Sealfon SC. Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data. NATURE COMPUTATIONAL SCIENCE 2023; 3:644-657. [PMID: 37974651 PMCID: PMC10653299 DOI: 10.1038/s43588-023-00476-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 06/06/2023] [Indexed: 11/19/2023]
Abstract
Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.
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Affiliation(s)
- Xi Chen
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Yuan Wang
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Antonio Cappuccio
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wan-Sze Cheng
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Venugopalan D. Nair
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Clare M. Miller
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Aliza B. Rubenstein
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - German Nudelman
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alicja Tadych
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Chandra L. Theesfeld
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Alexandria Vornholt
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Felicia Ruffin
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Michael Dagher
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Daniel G. Chawla
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | | | | | - Lishomwa C. Ndhlovu
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steven H. Kleinstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology and Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | | | - Irene Ramos
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Vance G. Fowler
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Christopher W. Woods
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- These authors jointly supervised this work: Elena Zaslavsky, Olga G. Troyanskaya, Stuart C. Sealfon
| | - Olga G. Troyanskaya
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- These authors jointly supervised this work: Elena Zaslavsky, Olga G. Troyanskaya, Stuart C. Sealfon
| | - Stuart C. Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- These authors jointly supervised this work: Elena Zaslavsky, Olga G. Troyanskaya, Stuart C. Sealfon
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Cai H, Des Marais DL. Revisiting regulatory coherence: accounting for temporal bias in plant gene co-expression analyses. THE NEW PHYTOLOGIST 2023; 238:16-24. [PMID: 36617750 DOI: 10.1111/nph.18720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 12/16/2022] [Indexed: 06/17/2023]
Affiliation(s)
- Haoran Cai
- Department of Civil and Environmental Engineering, MIT, 15 Vassar St., Cambridge, MA, 02139, USA
| | - David L Des Marais
- Department of Civil and Environmental Engineering, MIT, 15 Vassar St., Cambridge, MA, 02139, USA
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10
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McCalla SG, Fotuhi Siahpirani A, Li J, Pyne S, Stone M, Periyasamy V, Shin J, Roy S. Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data. G3 (BETHESDA, MD.) 2023; 13:jkad004. [PMID: 36626328 PMCID: PMC9997554 DOI: 10.1093/g3journal/jkad004] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 11/09/2022] [Accepted: 12/16/2022] [Indexed: 01/11/2023]
Abstract
Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.
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Affiliation(s)
- Sunnie Grace McCalla
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | | | - Jiaxin Li
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Saptarshi Pyne
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA
| | - Matthew Stone
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53792, USA
| | - Viswesh Periyasamy
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Junha Shin
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA
| | - Sushmita Roy
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53715, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53792, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
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11
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Disrupting the ArcA Regulatory Network Amplifies the Fitness Cost of Tetracycline Resistance in Escherichia coli. mSystems 2023; 8:e0090422. [PMID: 36537814 PMCID: PMC9948699 DOI: 10.1128/msystems.00904-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
There is an urgent need for strategies to discover secondary drugs to prevent or disrupt antimicrobial resistance (AMR), which is causing >700,000 deaths annually. Here, we demonstrate that tetracycline-resistant (TetR) Escherichia coli undergoes global transcriptional and metabolic remodeling, including downregulation of tricarboxylic acid cycle and disruption of redox homeostasis, to support consumption of the proton motive force for tetracycline efflux. Using a pooled genome-wide library of single-gene deletion strains, at least 308 genes, including four transcriptional regulators identified by our network analysis, were confirmed as essential for restoring the fitness of TetR E. coli during treatment with tetracycline. Targeted knockout of ArcA, identified by network analysis as a master regulator of this new compensatory physiological state, significantly compromised fitness of TetR E. coli during tetracycline treatment. A drug, sertraline, which generated a similar metabolome profile as the arcA knockout strain, also resensitized TetR E. coli to tetracycline. We discovered that the potentiating effect of sertraline was eliminated upon knocking out arcA, demonstrating that the mechanism of potential synergy was through action of sertraline on the tetracycline-induced ArcA network in the TetR strain. Our findings demonstrate that therapies that target mechanistic drivers of compensatory physiological states could resensitize AMR pathogens to lost antibiotics. IMPORTANCE Antimicrobial resistance (AMR) is projected to be the cause of >10 million deaths annually by 2050. While efforts to find new potent antibiotics are effective, they are expensive and outpaced by the rate at which new resistant strains emerge. There is desperate need for a rational approach to accelerate the discovery of drugs and drug combinations that effectively clear AMR pathogens and even prevent the emergence of new resistant strains. Using tetracycline-resistant (TetR) Escherichia coli, we demonstrate that gaining resistance is accompanied by loss of fitness, which is restored by compensatory physiological changes. We demonstrate that transcriptional regulators of the compensatory physiologic state are promising drug targets because their disruption increases the susceptibility of TetR E. coli to tetracycline. Thus, we describe a generalizable systems biology approach to identify new vulnerabilities within AMR strains to rationally accelerate the discovery of therapeutics that extend the life span of existing antibiotics.
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12
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Iyer MS, Pal A, Venkatesh KV. A Systems Biology Approach To Disentangle the Direct and Indirect Effects of Global Transcription Factors on Gene Expression in Escherichia coli. Microbiol Spectr 2023; 11:e0210122. [PMID: 36749045 PMCID: PMC10100776 DOI: 10.1128/spectrum.02101-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 01/19/2023] [Indexed: 02/08/2023] Open
Abstract
Delineating the pleiotropic effects of global transcriptional factors (TFs) is critical for understanding the system-wide regulatory response in a particular environment. Currently, with the availability of genome-wide TF binding and gene expression data for Escherichia coli, several gene targets can be assigned to the global TFs, albeit inconsistently. Here, using a systematic integrated approach with emphasis on metabolism, we characterized and quantified the direct effects as well as the growth rate-mediated indirect effects of global TFs using deletion mutants of FNR, ArcA, and IHF regulators (focal TFs) under glucose fermentative conditions. This categorization enabled us to disentangle the dense connections seen within the transcriptional regulatory network (TRN) and determine the exact nature of focal TF-driven epistatic interactions with other global and pathway-specific local regulators (iTFs). We extended our analysis to combinatorial deletions of these focal TFs to determine their cross talk effects as well as conserved patterns of regulatory interactions. Moreover, we predicted with high confidence several novel metabolite-iTF interactions using inferred iTF activity changes arising from the allosteric effects of the intracellular metabolites perturbed as a result of the absence of focal TFs. Further, using compendium level computational analyses, we revealed not only the coexpressed genes regulated by these focal TFs but also the coordination of the direct and indirect target expression in the context of the economy of intracellular metabolites. Overall, this study leverages the fundamentals of TF-driven regulation, which could serve as a better template for deciphering mechanisms underlying complex phenotypes. IMPORTANCE Understanding the pleiotropic effects of global TFs on gene expression and their relevance underlying a specific response in a particular environment has been challenging. Here, we distinguish the TF-driven direct effects and growth rate-mediated indirect effects on gene expression using single- and double-deletion mutants of FNR, ArcA, and IHF regulators under anaerobic glucose fermentation. Such dissection assists us in unraveling the precise nature of interactions existing between the focal TF(s) and several other TFs, including those altered by allosteric effects of intracellular metabolites. We were able to recapitulate the previously known metabolite-TF interactions and predict novel interactions with high confidence. Furthermore, we determined that the direct and indirect gene expression have a strong connection with each other when analyzed using the coexpressed- or coregulated-gene approach. Deciphering such regulatory patterns explicitly from the metabolism point of view would be valuable in understanding other unpredicted complex regulation existing in nature.
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Affiliation(s)
- Mahesh S. Iyer
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Ankita Pal
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - K. V. Venkatesh
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
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13
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Decoding transcriptional regulation via a human gene expression predictor. J Genet Genomics 2023; 50:305-317. [PMID: 36693565 DOI: 10.1016/j.jgg.2023.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 01/04/2023] [Accepted: 01/10/2023] [Indexed: 01/22/2023]
Abstract
Transcription factors (TFs) regulate cellular activities by controlling gene expression, but a predictive model describing how TFs quantitatively modulate human transcriptomes is lacking. We construct a universal human gene expression predictor and utilize it to decode transcriptional regulation. Using the expression of 1613 TFs, the predictor reconstitutes highly accurate transcriptomes for samples derived from a wide range of tissues and conditions. The broad applicability of the predictor indicates that it recapitulates the quantitative relationships between TFs and target genes ubiquitous across tissues. Significant interacting TF-target gene pairs are extracted from the predictor and enable downstream inference of TF regulators for diverse pathways involved in development, immunity, metabolism, and stress response. A detailed analysis of the hematopoiesis process reveals an atlas of key TFs regulating the development of different hematopoietic cell lineages, and a portion of these TFs are conserved between humans and mice. The results demonstrate that our method is capable of delineating the TFs responsible for fate determination. Compared to other existing tools, our approach shows better performance in recovering the correct TF regulators. Thus, we present a novel approach that can be used to study human transcriptional regulation in general.
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14
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NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity. Genome Biol 2022; 23:270. [PMID: 36575445 PMCID: PMC9793520 DOI: 10.1186/s13059-022-02835-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 12/05/2022] [Indexed: 12/28/2022] Open
Abstract
A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators' activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization.
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15
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NetREX-CF integrates incomplete transcription factor data with gene expression to reconstruct gene regulatory networks. Commun Biol 2022; 5:1282. [PMID: 36418514 PMCID: PMC9684490 DOI: 10.1038/s42003-022-04226-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 11/04/2022] [Indexed: 11/25/2022] Open
Abstract
The inference of Gene Regulatory Networks (GRNs) is one of the key challenges in systems biology. Leading algorithms utilize, in addition to gene expression, prior knowledge such as Transcription Factor (TF) DNA binding motifs or results of TF binding experiments. However, such prior knowledge is typically incomplete, therefore, integrating it with gene expression to infer GRNs remains difficult. To address this challenge, we introduce NetREX-CF-Regulatory Network Reconstruction using EXpression and Collaborative Filtering-a GRN reconstruction approach that brings together Collaborative Filtering to address the incompleteness of the prior knowledge and a biologically justified model of gene expression (sparse Network Component Analysis based model). We validated the NetREX-CF using Yeast data and then used it to construct the GRN for Drosophila Schneider 2 (S2) cells. To corroborate the GRN, we performed a large-scale RNA-Seq analysis followed by a high-throughput RNAi treatment against all 465 expressed TFs in the cell line. Our knockdown result has not only extensively validated the GRN we built, but also provides a benchmark that our community can use for evaluating GRNs. Finally, we demonstrate that NetREX-CF can infer GRNs using single-cell RNA-Seq, and outperforms other methods, by using previously published human data.
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16
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Papadakis Z, Etchebaster M, Garcia-Retortillo S. Cardiorespiratory Coordination in Collegiate Rowing: A Network Approach to Cardiorespiratory Exercise Testing. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13250. [PMID: 36293862 PMCID: PMC9603738 DOI: 10.3390/ijerph192013250] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/09/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
Collegiate rowing performance is often assessed by a cardiopulmonary exercise test (CPET). Rowers' on-water performance involves non-linear dynamic interactions and synergetic reconfigurations of the cardiorespiratory system. Cardiorespiratory coordination (CRC) method measures the co-variation among cardiorespiratory variables. Novice (n = 9) vs. Intermediate (n = 9) rowers' CRC (H0: Novice CRC = Intermediate CRC; HA: Novice CRC < Intermediate CRC) was evaluated through principal components analysis (PCA). A female NCAA Division II team (N = 18) grouped based on their off-water performance on 6000 m time trial. Rowers completed a customized CPET to exhaustion and a variety of cardiorespiratory values were recorded. The number of principal components (PCs) and respective PC eigenvalues per group were computed on SPSS vs28. Intermediate (77%) and Novice (33%) groups showed one PC1. Novice group formed an added PC2 due to the shift of expired fraction of oxygen or, alternatively, heart rate/ventilation, from the PC1 cluster of examined variables. Intermediate rowers presented a higher degree of CRC, possible due to their increased ability to utilize the bicarbonate buffering system during the CPET. CRC may be an alternative measure to assess aerobic fitness providing insights to the complex cardiorespiratory interactions involved in rowing during a CPET.
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Affiliation(s)
- Zacharias Papadakis
- Human Performance Laboratory, Department of Health Promotion and Clinical Practice, College of Health and Wellness, Barry University, Miami Shores, FL 33161, USA
| | - Michelle Etchebaster
- Human Performance Laboratory, Department of Health Promotion and Clinical Practice, College of Health and Wellness, Barry University, Miami Shores, FL 33161, USA
| | - Sergi Garcia-Retortillo
- Department of Health and Exercise Science, Wake Forest University, Winston-Salem, NC 27109, USA
- Complex Systems in Sport Research Group, Institut Nacional d’Educació Física de Catalunya (INEFC) University of Barcelona, 08007 Barcelona, Spain
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17
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Ahn-Horst TA, Mille LS, Sun G, Morrison JH, Covert MW. An expanded whole-cell model of E. coli links cellular physiology with mechanisms of growth rate control. NPJ Syst Biol Appl 2022; 8:30. [PMID: 35986058 PMCID: PMC9391491 DOI: 10.1038/s41540-022-00242-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/28/2022] [Indexed: 11/09/2022] Open
Abstract
Growth and environmental responses are essential for living organisms to survive and adapt to constantly changing environments. In order to simulate new conditions and capture dynamic responses to environmental shifts in a developing whole-cell model of E. coli, we incorporated additional regulation, including dynamics of the global regulator guanosine tetraphosphate (ppGpp), along with dynamics of amino acid biosynthesis and translation. With the model, we show that under perturbed ppGpp conditions, small molecule feedback inhibition pathways, in addition to regulation of expression, play a role in ppGpp regulation of growth. We also found that simulations with dysregulated amino acid synthesis pathways provide average amino acid concentration predictions that are comparable to experimental results but on the single-cell level, concentrations unexpectedly show regular fluctuations. Additionally, during both an upshift and downshift in nutrient availability, the simulated cell responds similarly with a transient increase in the mRNA:rRNA ratio. This additional simulation functionality should support a variety of new applications and expansions of the E. coli Whole-Cell Modeling Project.
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Affiliation(s)
- Travis A Ahn-Horst
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | | | - Gwanggyu Sun
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Jerry H Morrison
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
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18
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Bar N, Nikparvar B, Jayavelu ND, Roessler FK. Constrained Fourier estimation of short-term time-series gene expression data reduces noise and improves clustering and gene regulatory network predictions. BMC Bioinformatics 2022; 23:330. [PMID: 35945515 PMCID: PMC9364503 DOI: 10.1186/s12859-022-04839-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 07/12/2022] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Biological data suffers from noise that is inherent in the measurements. This is particularly true for time-series gene expression measurements. Nevertheless, in order to to explore cellular dynamics, scientists employ such noisy measurements in predictive and clustering tools. However, noisy data can not only obscure the genes temporal patterns, but applying predictive and clustering tools on noisy data may yield inconsistent, and potentially incorrect, results. RESULTS To reduce the noise of short-term (< 48 h) time-series expression data, we relied on the three basic temporal patterns of gene expression: waves, impulses and sustained responses. We constrained the estimation of the true signals to these patterns by estimating the parameters of first and second-order Fourier functions and using the nonlinear least-squares trust-region optimization technique. Our approach lowered the noise in at least 85% of synthetic time-series expression data, significantly more than the spline method ([Formula: see text]). When the data contained a higher signal-to-noise ratio, our method allowed downstream network component analyses to calculate consistent and accurate predictions, particularly when the noise variance was high. Conversely, these tools led to erroneous results from untreated noisy data. Our results suggest that at least 5-7 time points are required to efficiently de-noise logarithmic scaled time-series expression data. Investing in sampling additional time points provides little benefit to clustering and prediction accuracy. CONCLUSIONS Our constrained Fourier de-noising method helps to cluster noisy gene expression and interpret dynamic gene networks more accurately. The benefit of noise reduction is large and can constitute the difference between a successful application and a failing one.
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Affiliation(s)
- Nadav Bar
- grid.5947.f0000 0001 1516 2393Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Sælandsvei 4, Trondheim, NO-7491 Norway
| | - Bahareh Nikparvar
- grid.5947.f0000 0001 1516 2393Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Sælandsvei 4, Trondheim, NO-7491 Norway
| | - Naresh Doni Jayavelu
- grid.34477.330000000122986657Division of Medical Genetics, Department of Medicine, University of Washington Seattle, Seattle, WA 98195-7720 USA
| | - Fabienne Krystin Roessler
- grid.5947.f0000 0001 1516 2393Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Sælandsvei 4, Trondheim, NO-7491 Norway
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19
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Gan Y, Hu X, Zou G, Yan C, Xu G. Inferring Gene Regulatory Networks From Single-Cell Transcriptomic Data Using Bidirectional RNN. Front Oncol 2022; 12:899825. [PMID: 35692809 PMCID: PMC9178250 DOI: 10.3389/fonc.2022.899825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 04/22/2022] [Indexed: 11/30/2022] Open
Abstract
Accurate inference of gene regulatory rules is critical to understanding cellular processes. Existing computational methods usually decompose the inference of gene regulatory networks (GRNs) into multiple subproblems, rather than detecting potential causal relationships simultaneously, which limits the application to data with a small number of genes. Here, we propose BiRGRN, a novel computational algorithm for inferring GRNs from time-series single-cell RNA-seq (scRNA-seq) data. BiRGRN utilizes a bidirectional recurrent neural network to infer GRNs. The recurrent neural network is a complex deep neural network that can capture complex, non-linear, and dynamic relationships among variables. It maps neurons to genes, and maps the connections between neural network layers to the regulatory relationship between genes, providing an intuitive solution to model GRNs with biological closeness and mathematical flexibility. Based on the deep network, we transform the inference of GRNs into a regression problem, using the gene expression data at previous time points to predict the gene expression data at the later time point. Furthermore, we adopt two strategies to improve the accuracy and stability of the algorithm. Specifically, we utilize a bidirectional structure to integrate the forward and reverse inference results and exploit an incomplete set of prior knowledge to filter out some candidate inferences of low confidence. BiRGRN is applied to four simulated datasets and three real scRNA-seq datasets to verify the proposed method. We perform comprehensive comparisons between our proposed method with other state-of-the-art techniques. These experimental results indicate that BiRGRN is capable of inferring GRN simultaneously from time-series scRNA-seq data. Our method BiRGRN is implemented in Python using the TensorFlow machine-learning library, and it is freely available at https://gitee.com/DHUDBLab/bi-rgrn.
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Affiliation(s)
- Yanglan Gan
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Xin Hu
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Guobing Zou
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Cairong Yan
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Guangwei Xu
- School of Computer Science and Technology, Donghua University, Shanghai, China
- *Correspondence: Guangwei Xu,
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20
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Jiang X, Zhang X. RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique. BMC Bioinformatics 2022; 23:165. [PMID: 35524190 PMCID: PMC9074326 DOI: 10.1186/s12859-022-04696-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 04/25/2022] [Indexed: 11/29/2022] Open
Abstract
Background Current gene regulatory network (GRN) inference methods are notorious for a great number of indirect interactions hidden in the predictions. Filtering out the indirect interactions from direct ones remains an important challenge in the reconstruction of GRNs. To address this issue, we developed a redundancy silencing and network enhancement technique (RSNET) for inferring GRNs. Results To assess the performance of RSNET method, we implemented the experiments on several gold-standard networks by using simulation study, DREAM challenge dataset and Escherichia coli network. The results show that RSNET method performed better than the compared methods in sensitivity and accuracy. As a case of study, we used RSNET to construct functional GRN for apple fruit ripening from gene expression data. Conclusions In the proposed method, the redundant interactions including weak and indirect connections are silenced by recursive optimization adaptively, and the highly dependent nodes are constrained in the model to keep the real interactions. This study provides a useful tool for inferring clean networks. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04696-w.
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Affiliation(s)
- Xiaohan Jiang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China.,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan, 430074, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China. .,Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan, 430074, China.
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21
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Bano N, Fakhrah S, Mohanty CS, Bag SK. Transcriptome Meta-Analysis Associated Targeting Hub Genes and Pathways of Drought and Salt Stress Responses in Cotton ( Gossypium hirsutum): A Network Biology Approach. FRONTIERS IN PLANT SCIENCE 2022; 13:818472. [PMID: 35548277 PMCID: PMC9083274 DOI: 10.3389/fpls.2022.818472] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/21/2022] [Indexed: 06/12/2023]
Abstract
Abiotic stress tolerance is an intricate feature controlled through several genes and networks in the plant system. In abiotic stress, salt, and drought are well known to limit cotton productivity. Transcriptomics meta-analysis has arisen as a robust method to unravel the stress-responsive molecular network in crops. In order to understand drought and salt stress tolerance mechanisms, a meta-analysis of transcriptome studies is crucial. To confront these issues, here, we have given details of genes and networks associated with significant differential expression in response to salt and drought stress. The key regulatory hub genes of drought and salt stress conditions have notable associations with functional drought and salt stress-responsive (DSSR) genes. In the network study, nodulation signaling pathways 2 (NSP2), Dehydration-responsive element1 D (DRE1D), ethylene response factor (ERF61), cycling DOF factor 1 (CDF1), and tubby like protein 3 (TLP3) genes in drought and tubby like protein 1 (TLP1), thaumatin-like proteins (TLP), ethylene-responsive transcription factor ERF109 (EF109), ETS-Related transcription Factor (ELF4), and Arabidopsis thaliana homeodomain leucine-zipper gene (ATHB7) genes in salt showed the significant putative functions and pathways related to providing tolerance against drought and salt stress conditions along with the significant expression values. These outcomes provide potential candidate genes for further in-depth functional studies in cotton, which could be useful for the selection of an improved genotype of Gossypium hirsutum against drought and salt stress conditions.
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Affiliation(s)
- Nasreen Bano
- CSIR-National Botanical Research Institute (CSIR-NBRI), Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Shafquat Fakhrah
- CSIR-National Botanical Research Institute (CSIR-NBRI), Lucknow, India
- Department of Botany, University of Lucknow, Lucknow, India
| | - Chandra Sekhar Mohanty
- CSIR-National Botanical Research Institute (CSIR-NBRI), Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Sumit Kumar Bag
- CSIR-National Botanical Research Institute (CSIR-NBRI), Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
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22
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Quantitative metabolic fluxes regulated by trans-omic networks. Biochem J 2022; 479:787-804. [PMID: 35356967 PMCID: PMC9022981 DOI: 10.1042/bcj20210596] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/11/2022] [Accepted: 03/15/2022] [Indexed: 12/21/2022]
Abstract
Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.
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23
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Qin G, Knijnenburg TA, Gibbs DL, Moser R, Monnat RJ, Kemp CJ, Shmulevich I. A functional module states framework reveals transcriptional states for drug and target prediction. Cell Rep 2022; 38:110269. [PMID: 35045296 DOI: 10.1016/j.celrep.2021.110269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 08/24/2021] [Accepted: 12/23/2021] [Indexed: 11/03/2022] Open
Abstract
Cells are complex systems in which many functions are performed by different genetically defined and encoded functional modules. To systematically understand how these modules respond to drug or genetic perturbations, we develop a functional module states framework. Using this framework, we (1) define the drug-induced transcriptional state space for breast cancer cell lines using large public gene expression datasets and reveal that the transcriptional states are associated with drug concentration and drug targets, (2) identify potential targetable vulnerabilities through integrative analysis of transcriptional states after drug treatment and gene knockdown-associated cancer dependency, and (3) use functional module states to predict transcriptional state-dependent drug sensitivity and build prediction models for drug response. This approach demonstrates a similar prediction performance as approaches using high-dimensional gene expression values, with the added advantage of more clearly revealing biologically relevant transcriptional states and key regulators.
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Affiliation(s)
- Guangrong Qin
- Institute for Systems Biology, Seattle, WA 98109, USA.
| | | | - David L Gibbs
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Russell Moser
- Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Raymond J Monnat
- Department of Laboratory Medicine/Pathology & Genome Sciences, University of Washington, Seattle, WA 98195-7705, USA
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Wu Y, Xue L, Huang W, Deng M, Lin Y. Profiling transcription factor activity dynamics using intronic reads in time-series transcriptome data. PLoS Comput Biol 2022; 18:e1009762. [PMID: 35007289 PMCID: PMC8782462 DOI: 10.1371/journal.pcbi.1009762] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 01/21/2022] [Accepted: 12/15/2021] [Indexed: 11/19/2022] Open
Abstract
Activities of transcription factors (TFs) are temporally modulated to regulate dynamic cellular processes, including development, homeostasis, and disease. Recent developments of bioinformatic tools have enabled the analysis of TF activities using transcriptome data. However, because these methods typically use exon-based target expression levels, the estimated TF activities have limited temporal accuracy. To address this, we proposed a TF activity measure based on intron-level information in time-series RNA-seq data, and implemented it to decode the temporal control of TF activities during dynamic processes. We showed that TF activities inferred from intronic reads can better recapitulate instantaneous TF activities compared to the exon-based measure. By analyzing public and our own time-series transcriptome data, we found that intron-based TF activities improve the characterization of temporal phasing of cycling TFs during circadian rhythm, and facilitate the discovery of two temporally opposing TF modules during T cell activation. Collectively, we anticipate that the proposed approach would be broadly applicable for decoding global transcriptional architecture during dynamic processes.
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Affiliation(s)
- Yan Wu
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, China
| | - Lingfeng Xue
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Wen Huang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Minghua Deng
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- School of Mathematical Sciences and Center for Statistical Science, Peking University, Beijing, China
| | - Yihan Lin
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- The MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- * E-mail:
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25
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The Application of Machine Learning ICA-VMD in an Intelligent Diagnosis System in a Low SNR Environment. SENSORS 2021; 21:s21248344. [PMID: 34960438 PMCID: PMC8708876 DOI: 10.3390/s21248344] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/10/2021] [Accepted: 12/10/2021] [Indexed: 11/17/2022]
Abstract
This paper proposes a new method called independent component analysis-variational mode decomposition (ICA-VMD), which combines ICA and VMD. The purpose is to study the application of ICA-VMD in low signal-to-noise ratio (SNR) signal processing and data analysis. ICA is a very important method in the field of machine learning. It is an unsupervised learning algorithm that can dig out the independent factors hidden in the observation signal. The VMD method estimates each signal component by solving the frequency domain variational optimization problem, and it is very suitable for mechanical fault diagnosis. The advantage of ICA-VMD is that it requires two sensory cues to distinguish the original source from the unwanted noise. In the three cases studied here, the original source was first contaminated by white Gaussian noise. The three cases in this study are under different SNR conditions. The SNR in the first case is -6.46 dB, the SNR in the second case is -21.3728, and the SNR in the third case is -46.8177. The simulation results show that the ICA-VMD method can effectively recover the original source from the contaminated data. It is hoped that, in the future, there will be new discoveries and advances in science and technology to solve the noise interference problem through this method.
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Zheng X, Zhang C. Gene selection for microarray data classification via dual latent representation learning. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.07.047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Leifer I, Sánchez-Pérez M, Ishida C, Makse HA. Predicting synchronized gene coexpression patterns from fibration symmetries in gene regulatory networks in bacteria. BMC Bioinformatics 2021; 22:363. [PMID: 34238210 PMCID: PMC8265036 DOI: 10.1186/s12859-021-04213-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 05/19/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Gene regulatory networks coordinate the expression of genes across physiological states and ensure a synchronized expression of genes in cellular subsystems, critical for the coherent functioning of cells. Here we address the question whether it is possible to predict gene synchronization from network structure alone. We have recently shown that synchronized gene expression can be predicted from symmetries in the gene regulatory networks described by the concept of symmetry fibrations. We showed that symmetry fibrations partition the genes into groups called fibers based on the symmetries of their 'input trees', the set of paths in the network through which signals can reach a gene. In idealized dynamic gene expression models, all genes in a fiber are perfectly synchronized, while less idealized models-with gene input functions differencing between genes-predict symmetry breaking and desynchronization. RESULTS To study the functional role of gene fibers and to test whether some of the fiber-induced coexpression remains in reality, we analyze gene fibrations for the gene regulatory networks of E. coli and B. subtilis and confront them with expression data. We find approximate gene coexpression patterns consistent with symmetry fibrations with idealized gene expression dynamics. This shows that network structure alone provides useful information about gene synchronization, and suggest that gene input functions within fibers may be further streamlined by evolutionary pressures to realize a coexpression of genes. CONCLUSIONS Thus, gene fibrations provide a sound conceptual tool to describe tunable coexpression induced by network topology and shaped by mechanistic details of gene expression.
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Affiliation(s)
- Ian Leifer
- Levich Institute,Physics Department, City College of New York, New York, NY, 10031, USA
| | - Mishael Sánchez-Pérez
- Levich Institute,Physics Department, City College of New York, New York, NY, 10031, USA
- Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Mexico
| | - Cecilia Ishida
- Faculty of Medicine and Biomedical Sciences, Autonomous University of Chihuahua, 31125, Chihuahua, Chihuahua, Mexico
| | - Hernán A Makse
- Levich Institute,Physics Department, City College of New York, New York, NY, 10031, USA.
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Geng H, Wang M, Gong J, Xu Y, Ma S. An Arabidopsis expression predictor enables inference of transcriptional regulators for gene modules. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2021; 107:597-612. [PMID: 33974299 DOI: 10.1111/tpj.15315] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 03/08/2021] [Accepted: 05/05/2021] [Indexed: 06/12/2023]
Abstract
The regulation of gene expression by transcription factors (TFs) has been studied for a long time, but no model that can accurately predict transcriptome profiles based on TF activities currently exists. Here, we developed a computational approach, named EXPLICIT (Expression Prediction via Log-linear Combination of Transcription Factors), to construct a universal predictor for Arabidopsis to predict the expression of 29 182 non-TF genes using 1678 TFs. When applied to RNA-Seq samples from diverse tissues, EXPLICIT generated accurate predicted transcriptomes correlating well with actual expression, with an average correlation coefficient of 0.986. After recapitulating the quantitative relationships between TFs and their target genes, EXPLICIT enabled downstream inference of TF regulators for genes and gene modules functioning in diverse plant pathways, including those involved in suberin, flavonoid, glucosinolate metabolism, lateral root, xylem, secondary cell wall development or endoplasmic reticulum stress response. Our approach showed a better ability to recover the correct TF regulators when compared with existing plant tools, and provides an innovative way to study transcriptional regulation.
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Affiliation(s)
- Haiying Geng
- School of Life Sciences and Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
| | - Meng Wang
- School of Life Sciences and Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
| | - Jiazhen Gong
- School of Life Sciences and Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
| | - Yupu Xu
- School of Life Sciences and Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
| | - Shisong Ma
- School of Life Sciences and Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
- School of Data Science, University of Science and Technology of China, Hefei, China
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Ma CZ, Brent MR. Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data. Bioinformatics 2021; 37:1234-1245. [PMID: 33135076 PMCID: PMC8189679 DOI: 10.1093/bioinformatics/btaa947] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 09/26/2020] [Accepted: 10/27/2020] [Indexed: 12/20/2022] Open
Abstract
Motivation The activity of a transcription factor (TF) in a sample of cells is the extent to which it is exerting its regulatory potential. Many methods of inferring TF activity from gene expression data have been described, but due to the lack of appropriate large-scale datasets, systematic and objective validation has not been possible until now. Results We systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a condition-independent matrix of control strengths and a condition-dependent matrix of TF activity levels. We find that expression data in which the activities of individual TFs have been perturbed are both necessary and sufficient for obtaining good performance. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions, so the control strength matrices derived here can be used by others. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of yeast TFs Gcr2, Gln3, Gcn4 and Msn2. Availability and implementation Evaluation code and data are available at https://doi.org/10.5281/zenodo.4050573. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Cynthia Z Ma
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA
| | - Michael R Brent
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
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Gan Y, Xin Y, Hu X, Zou G. Inferring gene regulatory network from single-cell transcriptomic data by integrating multiple prior networks. Comput Biol Chem 2021; 93:107512. [PMID: 34044202 DOI: 10.1016/j.compbiolchem.2021.107512] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 05/12/2021] [Indexed: 11/29/2022]
Abstract
Gene regulatory network models the interactions between transcription factors and target genes. Reconstructing gene regulation network is critically important to understand gene function in a particular cellular context, providing key insights into complex biological systems. We develop a new computational method, named iMPRN, which integrates multiple prior networks to infer regulatory network. Based on the network component analysis model, iMPRN adopts linear regression, graph embedding, and elastic networks to optimize each prior network in line with specific biological context. For each rewired prior networks, iMPRN evaluate the confidence of the regulatory edges in each network based on B scores and finally integrated these optimized networks. We validate the effectiveness of iMPRN by comparing it with four widely-used gene regulatory network reconstruction algorithms on a simulation data set. The results show that iMPRN can infer the gene regulatory network more accurately. Further, on a real scRNA-seq dataset, iMPRN is respectively applied to reconstruct gene regulatory networks for malignant and nonmalignant head and neck tumor cells, demonstrating distinctive differences in their corresponding regulatory networks.
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Affiliation(s)
- Yanglan Gan
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Yongchang Xin
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Xin Hu
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Guobing Zou
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
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31
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Nikparvar B, Andreevskaya M, Duru IC, Bucur FI, Grigore-Gurgu L, Borda D, Nicolau AI, Riedel CU, Auvinen P, Bar N. Analysis of temporal gene regulation of Listeria monocytogenes revealed distinct regulatory response modes after exposure to high pressure processing. BMC Genomics 2021; 22:266. [PMID: 33853520 PMCID: PMC8045354 DOI: 10.1186/s12864-021-07461-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 02/10/2021] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND The pathogen Listeria (L.) monocytogenes is known to survive heat, cold, high pressure, and other extreme conditions. Although the response of this pathogen to pH, osmotic, temperature, and oxidative stress has been studied extensively, its reaction to the stress produced by high pressure processing HPP (which is a preservation method in the food industry), and the activated gene regulatory network (GRN) in response to this stress is still largely unknown. RESULTS We used RNA sequencing transcriptome data of L. monocytogenes (ScottA) treated at 400 MPa and 8∘C, for 8 min and combined it with current information in the literature to create a transcriptional regulation database, depicting the relationship between transcription factors (TFs) and their target genes (TGs) in L. monocytogenes. We then applied network component analysis (NCA), a matrix decomposition method, to reconstruct the activities of the TFs over time. According to our findings, L. monocytogenes responded to the stress applied during HPP by three statistically different gene regulation modes: survival mode during the first 10 min post-treatment, repair mode during 1 h post-treatment, and re-growth mode beyond 6 h after HPP. We identified the TFs and their TGs that were responsible for each of the modes. We developed a plausible model that could explain the regulatory mechanism that L. monocytogenes activated through the well-studied CIRCE operon via the regulator HrcA during the survival mode. CONCLUSIONS Our findings suggest that the timely activation of TFs associated with an immediate stress response, followed by the expression of genes for repair purposes, and then re-growth and metabolism, could be a strategy of L. monocytogenes to survive and recover extreme HPP conditions. We believe that our results give a better understanding of L. monocytogenes behavior after exposure to high pressure that may lead to the design of a specific knock-out process to target the genes or mechanisms. The results can help the food industry select appropriate HPP conditions to prevent L. monocytogenes recovery during food storage.
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Affiliation(s)
- Bahareh Nikparvar
- Department of Chemical Engineering, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Ilhan C Duru
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Florentina I Bucur
- Faculty of Food Science and Engineering, Dunarea de Jos University of Galati, Galati, Romania
| | - Leontina Grigore-Gurgu
- Faculty of Food Science and Engineering, Dunarea de Jos University of Galati, Galati, Romania
| | - Daniela Borda
- Faculty of Food Science and Engineering, Dunarea de Jos University of Galati, Galati, Romania
| | - Anca I Nicolau
- Faculty of Food Science and Engineering, Dunarea de Jos University of Galati, Galati, Romania
| | - Christian U Riedel
- Institute of Microbiology and Biotechnology, Ulm University, Ulm, Germany
| | - Petri Auvinen
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Nadav Bar
- Department of Chemical Engineering, Norwegian University of Science and Technology, Trondheim, Norway.
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Noh H, Hua Z, Chrysinas P, Shoemaker JE, Gunawan R. DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks. BMC Bioinformatics 2021; 22:108. [PMID: 33663384 PMCID: PMC7934467 DOI: 10.1186/s12859-021-04046-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 02/23/2021] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Knowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes. RESULTS In this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ uses a gene regulatory network model to identify direct perturbations to the transcription of genes using gene expression data. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course expression profiles, as well as leverage information on the gene network structure. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains. CONCLUSION DeltaNeTS+ is a powerful network analysis tool for inferring gene targets from gene expression profiles. As demonstrated in the case studies, by incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~ 10 s). Integrating static and dynamic expression data with transcriptional network structure extracted from genomic information, as enabled by DeltaNeTS+, is crucial toward personalized medicine, where treatments can be tailored to individual patients. DeltaNeTS+ can be freely downloaded from http://www.github.com/cabsel/deltanetsplus .
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Affiliation(s)
- Heeju Noh
- Institute for Chemical and Bioengineering, ETH Zurich, 8093 Zurich, Switzerland
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
- Present Address: Columbia University Medical Center, New York, NY 10032 USA
| | - Ziyi Hua
- Institute for Chemical and Bioengineering, ETH Zurich, 8093 Zurich, Switzerland
| | - Panagiotis Chrysinas
- Department of Chemical and Biological Engineering, University at Buffalo – SUNY, Buffalo, NY 14260 USA
| | - Jason E. Shoemaker
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 15261 USA
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15261 USA
| | - Rudiyanto Gunawan
- Department of Chemical and Biological Engineering, University at Buffalo – SUNY, Buffalo, NY 14260 USA
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How to outwit nature: Omics insight into butanol tolerance. Biotechnol Adv 2020; 46:107658. [PMID: 33220435 DOI: 10.1016/j.biotechadv.2020.107658] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 11/10/2020] [Accepted: 11/13/2020] [Indexed: 12/16/2022]
Abstract
The energy crisis, depletion of oil reserves, and global climate changes are pressing problems of developed societies. One possibility to counteract that is microbial production of butanol, a promising new fuel and alternative to many petrochemical reagents. However, the high butanol toxicity to all known microbial species is the main obstacle to its industrial implementation. The present state of the art review aims to expound the recent advances in modern omics approaches to resolving this insurmountable to date problem of low butanol tolerance. Genomics, transcriptomics, and proteomics show that butanol tolerance is a complex phenomenon affecting multiple genes and their expression. Efflux pumps, stress and multidrug response, membrane transport, and redox-related genes are indicated as being most important during butanol challenge, in addition to fine-tuning of global regulators of transcription (Spo0A, GntR), which may further improve tolerance. Lipidomics shows that the alterations in membrane composition (saturated lipids and plasmalogen increase) are very much species-specific and butanol-related. Glycomics discloses the pleiotropic effect of CcpA, the role of alternative sugar transport, and the production of exopolysaccharides as alternative routes to overcoming butanol stress. Unfortunately, the strain that simultaneously syntheses and tolerates butanol in concentrations that allow its commercialization has not yet been discovered or produced. Omics insight will allow the purposeful increase of butanol tolerance in natural and engineered producers and the effective heterologous expression of synthetic butanol pathways in strains hereditary butanol-resistant up to 3.2 - 4.9% (w/v). Future breakthrough can be achieved by a detailed study of the membrane proteome, of which 21% are proteins with unknown functions.
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Behjati Ardakani F, Kattler K, Heinen T, Schmidt F, Feuerborn D, Gasparoni G, Lepikhov K, Nell P, Hengstler J, Walter J, Schulz MH. Prediction of single-cell gene expression for transcription factor analysis. Gigascience 2020; 9:giaa113. [PMID: 33124660 PMCID: PMC7596801 DOI: 10.1093/gigascience/giaa113] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 08/20/2020] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Single-cell RNA sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from single-cell RNA data. RESULTS Here, we propose a novel approach for predicting gene expression at the single-cell level using cis-regulatory motifs, as well as epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single-cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by the literature. CONCLUSION Our proposed method allows us to identify distinct TFs that show cell type-specific regulation. This approach is not limited to TFs but can use any type of data that can potentially be used in explaining gene expression at the single-cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via https://github.com/SchulzLab/Triangulate.
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Affiliation(s)
- Fatemeh Behjati Ardakani
- Institute for Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany; Theodor-Stern-Kai 7
- Cluster of Excellence MMCI, Saarland University, Campus E1 7, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Campus E1 4, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Graduate School of Computer Science, Saarland University, Campus E1 3, Saarbrücken, Germany
| | - Kathrin Kattler
- Department of Genetics, Saarland University, Campus A2 4, 66123 Saarbrücken, Germany
| | - Tobias Heinen
- Cluster of Excellence MMCI, Saarland University, Campus E1 7, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Campus E1 4, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Florian Schmidt
- Institute for Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany; Theodor-Stern-Kai 7
- Cluster of Excellence MMCI, Saarland University, Campus E1 7, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Campus E1 4, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Graduate School of Computer Science, Saarland University, Campus E1 3, Saarbrücken, Germany
| | - David Feuerborn
- Leibniz Research Centre for Working Environment and Human Factors (IfADo), Ardeystraße 67, 44139 Dortmund, Germany
| | - Gilles Gasparoni
- Department of Genetics, Saarland University, Campus A2 4, 66123 Saarbrücken, Germany
| | - Konstantin Lepikhov
- Department of Genetics, Saarland University, Campus A2 4, 66123 Saarbrücken, Germany
| | - Patrick Nell
- Leibniz Research Centre for Working Environment and Human Factors (IfADo), Ardeystraße 67, 44139 Dortmund, Germany
| | - Jan Hengstler
- Leibniz Research Centre for Working Environment and Human Factors (IfADo), Ardeystraße 67, 44139 Dortmund, Germany
| | - Jörn Walter
- Department of Genetics, Saarland University, Campus A2 4, 66123 Saarbrücken, Germany
| | - Marcel H Schulz
- Institute for Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany; Theodor-Stern-Kai 7
- Cluster of Excellence MMCI, Saarland University, Campus E1 7, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Campus E1 4, Saarland Informatics Campus, 66123 Saarbrücken, Germany
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Shi M, Tan S, Xie XP, Li A, Yang W, Zhu T, Wang HQ. Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data. BMC Genomics 2020; 21:711. [PMID: 33054712 PMCID: PMC7559338 DOI: 10.1186/s12864-020-07079-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 09/18/2020] [Indexed: 12/02/2022] Open
Abstract
Background Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner. Results This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically discern direct and indirect regulations. Conclusions Extensive experimental results on simulation and real-world data demonstrate the effectiveness and efficiency of dlGRN in reverse engineering GRNs. A novel predicted transcription regulation between a TF TFAP2C and an oncogene EGFR was experimentally verified in lung cancer cells. Furthermore, the real application reveals the prevalence of DNA methylation regulation in gene regulatory system. dlGRN can be a standalone tool for GRN inference for its globalization and robustness.
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Affiliation(s)
- Ming Shi
- MICB Laboratory, Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, 350 Shushanghu Road, Hefei, Anhui, 230031, P. R. China.,Current Address: MOE Key Laboratory of Bioinformatics, Division of Bioinformatics and Center for Synthetic and Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Sheng Tan
- The CAS Key Laboratory of Innate Immunity and Chronic Disease, Division of Life Sciences and Medicine, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui, 230026, P. R. China
| | - Xin-Ping Xie
- School of Mathematics and Physics, Anhui Jianzhu University, 856 Jinzhai Road, Hefei, Anhui, 230022, P. R. China
| | - Ao Li
- School of Information Science and Technology, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui, 230026, P. R. China
| | - Wulin Yang
- Cancer hospital & Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, CAS, 350 Shushanghu Road, Hefei, Anhui, 230031, P. R. China
| | - Tao Zhu
- Current Address: MOE Key Laboratory of Bioinformatics, Division of Bioinformatics and Center for Synthetic and Systems Biology, TNLIST, Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Hong-Qiang Wang
- MICB Laboratory, Institute of Intelligent Machines, Hefei Institutes of Physical Science, CAS, 350 Shushanghu Road, Hefei, Anhui, 230031, P. R. China. .,Cancer hospital & Anhui Province Key Laboratory of Medical Physics and Technology, Center of Medical Physics and Technology, Hefei Institutes of Physical Science, CAS, 350 Shushanghu Road, Hefei, Anhui, 230031, P. R. China.
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Fortelny N, Bock C. Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data. Genome Biol 2020; 21:190. [PMID: 32746932 PMCID: PMC7397672 DOI: 10.1186/s13059-020-02100-5] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 07/10/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Deep learning has emerged as a versatile approach for predicting complex biological phenomena. However, its utility for biological discovery has so far been limited, given that generic deep neural networks provide little insight into the biological mechanisms that underlie a successful prediction. Here we demonstrate deep learning on biological networks, where every node has a molecular equivalent, such as a protein or gene, and every edge has a mechanistic interpretation, such as a regulatory interaction along a signaling pathway. RESULTS With knowledge-primed neural networks (KPNNs), we exploit the ability of deep learning algorithms to assign meaningful weights in multi-layered networks, resulting in a widely applicable approach for interpretable deep learning. We present a learning method that enhances the interpretability of trained KPNNs by stabilizing node weights in the presence of redundancy, enhancing the quantitative interpretability of node weights, and controlling for uneven connectivity in biological networks. We validate KPNNs on simulated data with known ground truth and demonstrate their practical use and utility in five biological applications with single-cell RNA-seq data for cancer and immune cells. CONCLUSIONS We introduce KPNNs as a method that combines the predictive power of deep learning with the interpretability of biological networks. While demonstrated here on single-cell sequencing data, this method is broadly relevant to other research areas where prior domain knowledge can be represented as networks.
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Affiliation(s)
- Nikolaus Fortelny
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.
- Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria.
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Plugging Small RNAs into the Network. mSystems 2020; 5:5/3/e00422-20. [PMID: 32487744 PMCID: PMC8534730 DOI: 10.1128/msystems.00422-20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Small RNAs (sRNAs) have been discovered in every bacterium examined and have been shown to play important roles in the regulation of a diverse range of behaviors, from metabolism to infection. However, despite a wide range of available techniques for discovering and validating sRNA regulatory interactions, only a minority of these molecules have been well characterized. In part, this is due to the nature of posttranscriptional regulation: the activity of an sRNA depends on the state of the transcriptome as a whole, so characterization is best carried out under the conditions in which it is naturally active. In this issue of mSystems, Arrieta-Ortiz and colleagues (M. L. Arrieta-Ortiz, C. Hafemeister, B. Shuster, N. S. Baliga, et al., mSystems 5:e00057-20, 2020, https://doi.org/10.1128/mSystems.00057-20) present a network inference approach based on estimating sRNA activity across transcriptomic compendia. This shows promise not only for identifying new sRNA regulatory interactions but also for pinpointing the conditions in which these interactions occur, providing a new avenue toward functional characterization of sRNAs.
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Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks. mSystems 2020; 5:5/3/e00057-20. [PMID: 32487739 PMCID: PMC8534726 DOI: 10.1128/msystems.00057-20] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator. This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimental conditions where sRNAs are most active. We uncover 45 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying 24 novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis. Overall, our strategy generates novel insights into the functional context of sRNA regulation in multiple bacterial species. IMPORTANCE Individual bacterial genomes can have dozens of small noncoding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions for inferring expanded sRNA regulons. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false-positive results. Due to its data-driven nature, our method prioritizes biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.
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Chen X, Gu J, Neuwald AF, Hilakivi-Clarke L, Clarke R, Xuan J. BICORN: An R package for integrative inference of de novo cis-regulatory modules. Sci Rep 2020; 10:7960. [PMID: 32409786 PMCID: PMC7224214 DOI: 10.1038/s41598-020-63043-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 01/15/2020] [Indexed: 12/18/2022] Open
Abstract
Genome-wide transcription factor (TF) binding signal analyses reveal co-localization of TF binding sites based on inferred cis-regulatory modules (CRMs). CRMs play a key role in understanding the cooperation of multiple TFs under specific conditions. However, the functions of CRMs and their effects on nearby gene transcription are highly dynamic and context-specific and therefore are challenging to characterize. BICORN (Bayesian Inference of COoperative Regulatory Network) builds a hierarchical Bayesian model and infers context-specific CRMs based on TF-gene binding events and gene expression data for a particular cell type. BICORN automatically searches for a list of candidate CRMs based on the input TF bindings at regulatory regions associated with genes of interest. Applying Gibbs sampling, BICORN iteratively estimates model parameters of CRMs, TF activities, and corresponding regulation on gene transcription, which it models as a sparse network of functional CRMs regulating target genes. The BICORN package is implemented in R (version 3.4 or later) and is publicly available on the CRAN server at https://cran.r-project.org/web/packages/BICORN/index.html.
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Affiliation(s)
- Xi Chen
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA
| | - Jinghua Gu
- Baylor Research Institute, 3310 Live Oak St, Dallas, TX, 75204, USA
| | - Andrew F Neuwald
- Institute for Genome Sciences and Department Biochemistry & Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Leena Hilakivi-Clarke
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3970 Reservoir Road, Washington, DC, 20057, USA
| | - Robert Clarke
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3970 Reservoir Road, Washington, DC, 20057, USA
| | - Jianhua Xuan
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA.
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Tsai MJ, Wang JR, Ho SJ, Shu LS, Huang WL, Ho SY. GREMA: modelling of emulated gene regulatory networks with confidence levels based on evolutionary intelligence to cope with the underdetermined problem. Bioinformatics 2020; 36:3833-3840. [DOI: 10.1093/bioinformatics/btaa267] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 04/14/2020] [Accepted: 05/09/2020] [Indexed: 11/12/2022] Open
Abstract
AbstractMotivationNon-linear ordinary differential equation (ODE) models that contain numerous parameters are suitable for inferring an emulated gene regulatory network (eGRN). However, the number of experimental measurements is usually far smaller than the number of parameters of the eGRN model that leads to an underdetermined problem. There is no unique solution to the inference problem for an eGRN using insufficient measurements.ResultsThis work proposes an evolutionary modelling algorithm (EMA) that is based on evolutionary intelligence to cope with the underdetermined problem. EMA uses an intelligent genetic algorithm to solve the large-scale parameter optimization problem. An EMA-based method, GREMA, infers a novel type of gene regulatory network with confidence levels for every inferred regulation. The higher the confidence level is, the more accurate the inferred regulation is. GREMA gradually determines the regulations of an eGRN with confidence levels in descending order using either an S-system or a Hill function-based ODE model. The experimental results showed that the regulations with high-confidence levels are more accurate and robust than regulations with low-confidence levels. Evolutionary intelligence enhanced the mean accuracy of GREMA by 19.2% when using the S-system model with benchmark datasets. An increase in the number of experimental measurements may increase the mean confidence level of the inferred regulations. GREMA performed well compared with existing methods that have been previously applied to the same S-system, DREAM4 challenge and SOS DNA repair benchmark datasets.Availability and implementationAll of the datasets that were used and the GREMA-based tool are freely available at https://nctuiclab.github.io/GREMA.Supplementary informationSupplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ming-Ju Tsai
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan
| | - Jyun-Rong Wang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan
| | - Shinn-Jang Ho
- Department of Automation Engineering, National Formosa University, Yunlin 632, Taiwan
| | - Li-Sun Shu
- Department of Information Management, Overseas Chinese University, Taichung 407, Taiwan
| | - Wen-Lin Huang
- Department of Industrial Engineering and Management, Minghsin University of Science and Technology, Xinfeng 304, Taiwan
| | - Shinn-Ying Ho
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan
- Department of Biological Science and Technology
- Center For Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Chiao Tung University, Hsinchu 300, Taiwan
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Kang Y, Patel NR, Shively C, Recio PS, Chen X, Wranik BJ, Kim G, McIsaac RS, Mitra R, Brent MR. Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses. Genome Res 2020; 30:459-471. [PMID: 32060051 PMCID: PMC7111528 DOI: 10.1101/gr.259655.119] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 02/11/2020] [Indexed: 12/22/2022]
Abstract
A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human, but they rarely converge on a common set of direct, functional targets for a TF. Even the few genes that are both bound and responsive may not be direct functional targets. Our analysis shows that when there are many nonfunctional binding sites and many indirect targets, nonfunctional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce dual threshold optimization (DTO), a new method for setting significance thresholds on binding and perturbation-response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that have been processed by network inference algorithms, which further improves convergence. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. These new data sets improve convergence and expand the high-confidence network synergistically.
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Affiliation(s)
- Yiming Kang
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, Missouri 63130, USA
| | - Nikhil R Patel
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, Missouri 63130, USA
| | - Christian Shively
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Pamela Samantha Recio
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Xuhua Chen
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Bernd J Wranik
- Calico Life Sciences LLC, South San Francisco, California 94080, USA
| | - Griffin Kim
- Calico Life Sciences LLC, South San Francisco, California 94080, USA
| | - R Scott McIsaac
- Calico Life Sciences LLC, South San Francisco, California 94080, USA
| | - Robi Mitra
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Michael R Brent
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, Missouri 63130, USA
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Sun Q, Kong W, Mou X, Wang S. Transcriptional Regulation Analysis of Alzheimer's Disease Based on FastNCA Algorithm. Curr Bioinform 2019. [DOI: 10.2174/1574893614666190919150411] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background:
Understanding the relationship between genetic variation and gene expression
is a central issue in genetics. Although many studies have identified genetic variations associated
with gene expression, it is unclear how they perturb the underlying regulatory network of
gene expression.
Objective:
To explore how genetic variations perturb potential transcriptional regulation networks
of Alzheimer’s disease (AD) to paint a more complete picture of the complex landscape of transcription
regulation.
Methods:
Fast network component analysis (FastNCA), which can capture the genetic variations
in the form of single nucleotide polymorphisms (SNPs), is applied to analyse the expression activities
of TFs and their regulatory strengths on TGs using microarray and RNA-seq data of AD.
Then, multi-data fusion analysis was used to analyze the different TGs regulated by the same TFs
in the different data by constructing the transcriptional regulatory networks of differentially expressed
genes.
Results:
the common TF regulating TGs are not necessarily identical in different data, they may be
involved in the same pathways that are closely related to the pathogenesis of AD, such as immune
response, signal transduction and cytokine-cytokine receptor interaction pathways. Even if they are
involved in different pathways, these pathways are also confirmed to have a potential link with
AD.
Conclusion:
The study shows that the pathways of different TGs regulated by the same TFs in different
data are all closely related to AD. Multi-data fusion analysis can form a certain complement
to some extent and get more comprehensive results in the process of exploring the pathogenesis
of AD.
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Affiliation(s)
- Qianni Sun
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai 201306, China
| | - Wei Kong
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai 201306, China
| | - Xiaoyang Mou
- Department of Biochemistry, Rowan University and Guava Medicine, Glassboro, New Jersey 08028, United States
| | - Shuaiqun Wang
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai 201306, China
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Lempp M, Farke N, Kuntz M, Freibert SA, Lill R, Link H. Systematic identification of metabolites controlling gene expression in E. coli. Nat Commun 2019; 10:4463. [PMID: 31578326 PMCID: PMC6775132 DOI: 10.1038/s41467-019-12474-1] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 09/11/2019] [Indexed: 01/07/2023] Open
Abstract
Metabolism controls gene expression through allosteric interactions between metabolites and transcription factors. These interactions are usually measured with in vitro assays, but there are no methods to identify them at a genome-scale in vivo. Here we show that dynamic transcriptome and metabolome data identify metabolites that control transcription factors in E. coli. By switching an E. coli culture between starvation and growth, we induce strong metabolite concentration changes and gene expression changes. Using Network Component Analysis we calculate the activities of 209 transcriptional regulators and correlate them with metabolites. This approach captures, for instance, the in vivo kinetics of CRP regulation by cyclic-AMP. By testing correlations between all pairs of transcription factors and metabolites, we predict putative effectors of 71 transcription factors, and validate five interactions in vitro. These results show that combining transcriptomics and metabolomics generates hypotheses about metabolism-transcription interactions that drive transitions between physiological states. Interactions between metabolites and transcription factors are known to control gene expression but analyzing these events at genome-scale is challenging. Here, the authors integrate dynamic metabolome and transcriptome data from E.coli to predict regulatory metabolite-transcription factor interactions.
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Affiliation(s)
- Martin Lempp
- Max Planck Institute for Terrestrial Microbiology, Marburg, 35043, Germany
| | - Niklas Farke
- Max Planck Institute for Terrestrial Microbiology, Marburg, 35043, Germany
| | - Michelle Kuntz
- Max Planck Institute for Terrestrial Microbiology, Marburg, 35043, Germany
| | - Sven Andreas Freibert
- Institut für Zytobiologie und Zytopathologie, Philipps-Universität Marburg, 35033, Marburg, Germany
| | - Roland Lill
- Institut für Zytobiologie und Zytopathologie, Philipps-Universität Marburg, 35033, Marburg, Germany.,LOEWE Zentrum für Synthetische Mikrobiologie SYNMIKRO, Philipps-Universität Marburg, 35032, Marburg, Germany
| | - Hannes Link
- Max Planck Institute for Terrestrial Microbiology, Marburg, 35043, Germany. .,LOEWE Zentrum für Synthetische Mikrobiologie SYNMIKRO, Philipps-Universität Marburg, 35032, Marburg, Germany.
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Poon ENY, Hao B, Guan D, Jun Li M, Lu J, Yang Y, Wu B, Wu SCM, Webb SE, Liang Y, Miller AL, Yao X, Wang J, Yan B, Boheler KR. Integrated transcriptomic and regulatory network analyses identify microRNA-200c as a novel repressor of human pluripotent stem cell-derived cardiomyocyte differentiation and maturation. Cardiovasc Res 2019; 114:894-906. [PMID: 29373717 DOI: 10.1093/cvr/cvy019] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 01/22/2018] [Indexed: 11/12/2022] Open
Abstract
Aims MicroRNAs (miRNAs) are crucial for the post-transcriptional control of protein-encoding genes and together with transcription factors (TFs) regulate gene expression; however, the regulatory activities of miRNAs during cardiac development are only partially understood. In this study, we tested the hypothesis that integrative computational approaches could identify miRNAs that experimentally could be shown to regulate cardiomyogenesis. Methods and results We integrated expression profiles with bioinformatics analyses of miRNA and TF regulatory programs to identify candidate miRNAs involved with cardiac development. Expression profiling showed that miR-200c, which is not normally detected in adult heart, is progressively down-regulated both during cardiac development and in vitro differentiation of human embryonic stem cells (hESCs) to cardiomyocytes (CMs). We employed computational methodologies to predict target genes of both miR-200c and five key cardiac TFs to identify co-regulated gene networks. The inferred cardiac networks revealed that the cooperative action of miR-200c with these five key TFs, including three (GATA4, SRF and TBX5) targeted by miR-200c, should modulate key processes and pathways necessary for CM development and function. Experimentally, over-expression (OE) of miR-200c in hESC-CMs reduced the mRNA levels of GATA4, SRF and TBX5. Cardiac expression of Ca2+, K+ and Na+ ion channel genes (CACNA1C, KCNJ2 and SCN5A) were also significantly altered by knockdown or OE of miR-200c. Luciferase reporter assays validated miR-200c binding sites on the 3' untranslated region of CACNA1C. In hESC-CMs, elevated miR-200c increased beating frequency, and repressed both Ca2+ influx, mediated by the L-type Ca2+ channel and Ca2+ transients. Conclusions Our analyses demonstrate that miR-200c represses hESC-CM differentiation and maturation. The integrative computation and experimental approaches described here, when applied more broadly, will enhance our understanding of the interplays between miRNAs and TFs in controlling cardiac development and disease processes.
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Affiliation(s)
- Ellen Ngar-Yun Poon
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Department of Paediatrics and Adolescent Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Baixia Hao
- Division of Life Science and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Daogang Guan
- School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
| | - Mulin Jun Li
- Department of Pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.,Centre of Genomics Sciences, LKS Faculty of Medicine, The University of Hong Kong. Hong Kong, China
| | - Jun Lu
- School of Biomedical Sciences, LSK Institute of Health Science, The Chinese University of Hong Kong, Hong Kong, China
| | - Yong Yang
- Laboratory for Food Safety and Environmental Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave, Shenzhen, Guangdong 518055, China
| | - Binbin Wu
- Laboratory for Food Safety and Environmental Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave, Shenzhen, Guangdong 518055, China
| | - Stanley Chun-Ming Wu
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Sarah E Webb
- Division of Life Science and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Yan Liang
- Laboratory for Food Safety and Environmental Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave, Shenzhen, Guangdong 518055, China
| | - Andrew L Miller
- Division of Life Science and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.,Marine Biology Laboratory, Woods Hole, MA 02543, USA
| | - Xiaoqiang Yao
- School of Biomedical Sciences, LSK Institute of Health Science, The Chinese University of Hong Kong, Hong Kong, China
| | - Junwen Wang
- Centre of Genomics Sciences, LKS Faculty of Medicine, The University of Hong Kong. Hong Kong, China.,Center for Individualized Medicine, Department of Health Sciences Research, Mayo Clinic, Scottsdale, AZ 85259, USA and Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA
| | - Bin Yan
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Centre of Genomics Sciences, LKS Faculty of Medicine, The University of Hong Kong. Hong Kong, China.,Laboratory for Food Safety and Environmental Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Ave, Shenzhen, Guangdong 518055, China
| | - Kenneth R Boheler
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China.,Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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Duren Z, Wang Y, Wang J, Zhao XM, Lv L, Li X, Liu J, Zhu XG, Chen L, Wang Y. Hierarchical graphical model reveals HFR1 bridging circadian rhythm and flower development in Arabidopsis thaliana. NPJ Syst Biol Appl 2019; 5:28. [PMID: 31428455 PMCID: PMC6690920 DOI: 10.1038/s41540-019-0106-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 07/23/2019] [Indexed: 01/02/2023] Open
Abstract
To study systems-level properties of the cell, it is necessary to go beyond individual regulators and target genes to study the regulatory network among transcription factors (TFs). However, it is difficult to directly dissect the TFs mediated genome-wide gene regulatory network (GRN) by experiment. Here, we proposed a hierarchical graphical model to estimate TF activity from mRNA expression by building TF complexes with protein cofactors and inferring TF's downstream regulatory network simultaneously. Then we applied our model on flower development and circadian rhythm processes in Arabidopsis thaliana. The computational results show that the sequence specific bHLH family TF HFR1 recruits the chromatin regulator HAC1 to flower development master regulator TF AG and further activates AG's expression by histone acetylation. Both independent data and experimental results supported this discovery. We also found a flower tissue specific H3K27ac ChIP-seq peak at AG gene body and a HFR1 motif in the center of this H3K27ac peak. Furthermore, we verified that HFR1 physically interacts with HAC1 by yeast two-hybrid experiment. This HFR1-HAC1-AG triplet relationship may imply that flower development and circadian rhythm are bridged by epigenetic regulation and enrich the classical ABC model in flower development. In addition, our TF activity network can serve as a general method to elucidate molecular mechanisms on other complex biological regulatory processes.
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Affiliation(s)
- Zhana Duren
- CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Yaling Wang
- State Key Laboratory of Molecular Plant Sciences and Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, 200032 China
| | - Jiguang Wang
- Division of Life Science, Department of Chemical and Biological Engineering, Center of Systems Biology and Human Health, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433 China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Shanghai, China
| | - Le Lv
- Bayer U.S. – Crop Science, Monsanto Legal Entity, St. Louis, MO 63156 USA
| | - Xiaobo Li
- Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305 USA
| | - Jingdong Liu
- Bayer U.S. – Crop Science, Monsanto Legal Entity, St. Louis, MO 63156 USA
| | - Xin-Guang Zhu
- State Key Laboratory of Molecular Plant Sciences and Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, 200032 China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, 200031 China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223 China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210 China
- Research Center for Brain Science and Brain-Inspired Intelligence, 201210 Shanghai, China
| | - Yong Wang
- CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223 China
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Chen X, Gu J, Wang X, Jung JG, Wang TL, Hilakivi-Clarke L, Clarke R, Xuan J. CRNET: an efficient sampling approach to infer functional regulatory networks by integrating large-scale ChIP-seq and time-course RNA-seq data. Bioinformatics 2019; 34:1733-1740. [PMID: 29280996 DOI: 10.1093/bioinformatics/btx827] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Accepted: 12/20/2017] [Indexed: 12/28/2022] Open
Abstract
Motivation NGS techniques have been widely applied in genetic and epigenetic studies. Multiple ChIP-seq and RNA-seq profiles can now be jointly used to infer functional regulatory networks (FRNs). However, existing methods suffer from either oversimplified assumption on transcription factor (TF) regulation or slow convergence of sampling for FRN inference from large-scale ChIP-seq and time-course RNA-seq data. Results We developed an efficient Bayesian integration method (CRNET) for FRN inference using a two-stage Gibbs sampler to estimate iteratively hidden TF activities and the posterior probabilities of binding events. A novel statistic measure that jointly considers regulation strength and regression error enables the sampling process of CRNET to converge quickly, thus making CRNET very efficient for large-scale FRN inference. Experiments on synthetic and benchmark data showed a significantly improved performance of CRNET when compared with existing methods. CRNET was applied to breast cancer data to identify FRNs functional at promoter or enhancer regions in breast cancer MCF-7 cells. Transcription factor MYC is predicted as a key functional factor in both promoter and enhancer FRNs. We experimentally validated the regulation effects of MYC on CRNET-predicted target genes using appropriate RNAi approaches in MCF-7 cells. Availability and implementation R scripts of CRNET are available at http://www.cbil.ece.vt.edu/software.htm. Contact xuan@vt.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xi Chen
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Jinghua Gu
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Xiao Wang
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Jin-Gyoung Jung
- Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, USA
| | - Tian-Li Wang
- Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, USA
| | - Leena Hilakivi-Clarke
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA
| | - Robert Clarke
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057, USA
| | - Jianhua Xuan
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
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Smita S, Katiyar A, Lenka SK, Dalal M, Kumar A, Mahtha SK, Yadav G, Chinnusamy V, Pandey DM, Bansal KC. Gene network modules associated with abiotic stress response in tolerant rice genotypes identified by transcriptome meta-analysis. Funct Integr Genomics 2019; 20:29-49. [PMID: 31286320 DOI: 10.1007/s10142-019-00697-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 05/31/2019] [Accepted: 06/19/2019] [Indexed: 10/26/2022]
Abstract
Abiotic stress tolerance is a complex trait regulated by multiple genes and gene networks in plants. A range of abiotic stresses are known to limit rice productivity. Meta-transcriptomics has emerged as a powerful approach to decipher stress-associated molecular network in model crops. However, retaining specificity of gene expression in tolerant and susceptible genotypes during meta-transcriptome analysis is important for understanding genotype-dependent stress tolerance mechanisms. Addressing this aspect, we describe here "abiotic stress tolerant" (ASTR) genes and networks specifically and differentially expressing in tolerant rice genotypes in response to different abiotic stress conditions. We identified 6,956 ASTR genes, key hub regulatory genes, transcription factors, and functional modules having significant association with abiotic stress-related ontologies and cis-motifs. Out of the 6956 ASTR genes, 73 were co-located within the boundary of previously identified abiotic stress trait-related quantitative trait loci. Functional annotation of 14 uncharacterized ASTR genes is proposed using multiple computational methods. Around 65% of the top ASTR genes were found to be differentially expressed in at least one of the tolerant genotypes under different stress conditions (cold, salt, drought, or heat) from publicly available RNAseq data comparison. The candidate ASTR genes specifically associated with tolerance could be utilized for engineering rice and possibly other crops for broad-spectrum tolerance to abiotic stresses.
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Affiliation(s)
- Shuchi Smita
- ICAR-National Bureau of Plant Genetic Resources, Indian Agricultural Research Institute Campus, New Delhi, 110012, India
- Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Amit Katiyar
- ICAR-National Bureau of Plant Genetic Resources, Indian Agricultural Research Institute Campus, New Delhi, 110012, India
- Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India
- ICMR-AIIMS Computational Genomics Center, Div. of I.S.R.M., Indian Council of Medical Research, Ansari Nagar, New Delhi, 110029, India
| | - Sangram Keshari Lenka
- TERI-Deakin Nanobiotechnology Center, The Energy and Resources Institute, Gurgaon, Haryana, 122001, India
| | - Monika Dalal
- ICAR-National Research Center on Plant Biotechnology, Indian Agricultural Research Institute Campus, New Delhi, 110012, India
| | - Amish Kumar
- Computational Biology Laboratory, National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, 110067, India
| | - Sanjeet Kumar Mahtha
- Computational Biology Laboratory, National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, 110067, India
| | - Gitanjali Yadav
- Computational Biology Laboratory, National Institute of Plant Genome Research, Aruna Asaf Ali Marg, New Delhi, 110067, India
| | - Viswanathan Chinnusamy
- ICAR-Division of Plant Physiology, Indian Agricultural Research Institute, New Delhi, 110012, India.
| | - Dev Mani Pandey
- Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India
| | - Kailash Chander Bansal
- ICAR-National Bureau of Plant Genetic Resources, Indian Agricultural Research Institute Campus, New Delhi, 110012, India.
- TERI-Deakin Nanobiotechnology Center, The Energy and Resources Institute, Gurgaon, Haryana, 122001, India.
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48
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Gene selection for microarray data classification via adaptive hypergraph embedded dictionary learning. Gene 2019; 706:188-200. [DOI: 10.1016/j.gene.2019.04.060] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 04/03/2019] [Accepted: 04/22/2019] [Indexed: 01/19/2023]
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49
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Abstract
Bacterial biofilms are ubiquitous in natural environments and play an important role in many clinical, industrial, and ecological settings. Although much is known about the transcriptional regulatory networks that control biofilm formation in model bacteria such as Bacillus subtilis, very little is known about the role of metabolism in this complex developmental process. To address this important knowledge gap, we performed a time-resolved analysis of the metabolic changes associated with bacterial biofilm development in B. subtilis by combining metabolomic, transcriptomic, and proteomic analyses. Here, we report a widespread and dynamic remodeling of metabolism affecting central carbon metabolism, primary biosynthetic pathways, fermentation pathways, and secondary metabolism. This report serves as a unique hypothesis-generating resource for future studies on bacterial biofilm physiology. Outside the biofilm research area, this work should also prove relevant to any investigators interested in microbial physiology and metabolism. Biofilms are structured communities of tightly associated cells that constitute the predominant state of bacterial growth in natural and human-made environments. Although the core genetic circuitry that controls biofilm formation in model bacteria such as Bacillus subtilis has been well characterized, little is known about the role that metabolism plays in this complex developmental process. Here, we performed a time-resolved analysis of the metabolic changes associated with pellicle biofilm formation and development in B. subtilis by combining metabolomic, transcriptomic, and proteomic analyses. We report surprisingly widespread and dynamic remodeling of metabolism affecting central carbon metabolism, primary biosynthetic pathways, fermentation pathways, and secondary metabolism. Most of these metabolic alterations were hitherto unrecognized as biofilm associated. For example, we observed increased activity of the tricarboxylic acid (TCA) cycle during early biofilm growth, a shift from fatty acid biosynthesis to fatty acid degradation, reorganization of iron metabolism and transport, and a switch from acetate to acetoin fermentation. Close agreement between metabolomic, transcriptomic, and proteomic measurements indicated that remodeling of metabolism during biofilm development was largely controlled at the transcriptional level. Our results also provide insights into the transcription factors and regulatory networks involved in this complex metabolic remodeling. Following upon these results, we demonstrated that acetoin production via acetolactate synthase is essential for robust biofilm growth and has the dual role of conserving redox balance and maintaining extracellular pH. This report represents a comprehensive systems-level investigation of the metabolic remodeling occurring during B. subtilis biofilm development that will serve as a useful road map for future studies on biofilm physiology.
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50
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Ortmayr K, Dubuis S, Zampieri M. Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism. Nat Commun 2019; 10:1841. [PMID: 31015463 PMCID: PMC6478870 DOI: 10.1038/s41467-019-09695-9] [Citation(s) in RCA: 81] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 03/22/2019] [Indexed: 12/20/2022] Open
Abstract
Transcriptional reprogramming of cellular metabolism is a hallmark of cancer. However, systematic approaches to study the role of transcriptional regulators (TRs) in mediating cancer metabolic rewiring are missing. Here, we chart a genome-scale map of TR-metabolite associations in human cells using a combined computational-experimental framework for large-scale metabolic profiling of adherent cell lines. By integrating intracellular metabolic profiles of 54 cancer cell lines with transcriptomic and proteomic data, we unraveled a large space of associations between TRs and metabolic pathways. We found a global regulatory signature coordinating glucose- and one-carbon metabolism, suggesting that regulation of carbon metabolism in cancer may be more diverse and flexible than previously appreciated. Here, we demonstrate how this TR-metabolite map can serve as a resource to predict TRs potentially responsible for metabolic transformation in patient-derived tumor samples, opening new opportunities in understanding disease etiology, selecting therapeutic treatments and in designing modulators of cancer-related TRs. Aberrant gene expression in cancer coincides with drastic changes in metabolism. Here, the authors combined metabolome, transcriptome and proteome data in 54 cancer cell lines to uncover a genome-scale network of associations between transcriptional regulators and metabolites.
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Affiliation(s)
- Karin Ortmayr
- Institute of Molecular Systems Biology, ETH Zurich, Otto-Stern-Weg 3, CH-8093, Zurich, Switzerland
| | - Sébastien Dubuis
- Institute of Molecular Systems Biology, ETH Zurich, Otto-Stern-Weg 3, CH-8093, Zurich, Switzerland
| | - Mattia Zampieri
- Institute of Molecular Systems Biology, ETH Zurich, Otto-Stern-Weg 3, CH-8093, Zurich, Switzerland.
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